# Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions

arXiv: https://arxiv.org/abs/2307.00014 · HTML: https://mlsanity.com/paper/2307.00014/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 155 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 30 of 33)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem](https://mlsanity.com/paper/1701.08376/) (2017)
- [Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey](https://mlsanity.com/paper/2001.02319/) (2020)
- [Neural Networks for Parameter Estimation in Intractable Models](https://mlsanity.com/paper/2107.14346/) (2021)
- [AI-IMU Dead-Reckoning](https://mlsanity.com/paper/1904.06064/) (2019)
- [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Deep Learning-based Spacecraft Relative Navigation Methods: A Survey](https://mlsanity.com/paper/2108.08876/) (2021)
- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [A Hybrid Model and Learning-Based Adaptive Navigation Filter](https://mlsanity.com/paper/2207.12082/) (2022)
- [RINS-W: Robust Inertial Navigation System on Wheels](https://mlsanity.com/paper/1903.02210/) (2019)
- [Data-Driven Denoising of Stationary Accelerometer Signals](https://mlsanity.com/paper/2206.05937/) (2022)
- [From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)](https://mlsanity.com/paper/2002.11310/) (2020)
- [Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation](https://mlsanity.com/paper/2205.07883/) (2022)
- [Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios](https://mlsanity.com/paper/2212.11671/) (2022)
- [RIANN -- A Robust Neural Network Outperforms Attitude Estimation Filters](https://mlsanity.com/paper/2104.07391/) (2021)
- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements](https://mlsanity.com/paper/2210.11572/) (2022)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Information Aided Navigation: A Review](https://mlsanity.com/paper/2301.01114/) (2023)
- [End-to-End Velocity Estimation For Autonomous Racing](https://mlsanity.com/paper/2003.06917/) (2020)
- [UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry](https://mlsanity.com/paper/2109.01533/) (2021)
- [A Deep Learning Approach To Dead-Reckoning Navigation For Autonomous Underwater Vehicles With Limited Sensor Payloads](https://mlsanity.com/paper/2110.00661/) (2021)
- [A Learning-based approach for bias elimination in low cost gyroscopes](https://mlsanity.com/paper/2210.04568/) (2022)
- [Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6DoF Inertial Measurement Units](https://mlsanity.com/paper/2302.06037/) (2023)
- [TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation](https://mlsanity.com/paper/2304.07728/) (2023)
- [LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation](https://mlsanity.com/paper/2209.08816/) (2022)
- [ProNet: Adaptive Process Noise Estimation for INS/DVL Fusion](https://mlsanity.com/paper/2212.08882/) (2022)

## Built on by (most cited first, 21 of 21)

- [Adaptive Kalman-Informed Transformer](https://mlsanity.com/paper/2401.09987/) (2024)
- [Rapid Gyroscope Calibration: A Deep Learning Approach](https://mlsanity.com/paper/2409.00488/) (2024)
- [DCNet: A Data-Driven Framework for DVL Calibration](https://mlsanity.com/paper/2410.08809/) (2024)
- [Learning IMU Bias with Diffusion Model](https://mlsanity.com/paper/2505.11763/) (2025)
- [MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation](https://mlsanity.com/paper/2507.18206/) (2025)
- [On Neural Inertial Classification Networks for Pedestrian Activity Recognition](https://mlsanity.com/paper/2502.17520/) (2025)
- [Data-Driven Strategies for Coping with Incomplete DVL Measurements](https://mlsanity.com/paper/2401.15620/) (2024)
- [WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning](https://mlsanity.com/paper/2503.13568/) (2025)
- [PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms](https://mlsanity.com/paper/2601.03040/) (2026)
- [Data-Driven Gyroscope Calibration](https://mlsanity.com/paper/2410.12485/) (2024)
- [Estimation of Food Intake Quantity Using Inertial Signals from Smartwatches](https://mlsanity.com/paper/2502.06649/) (2025)
- [Gaussian Process Regression for Improved Underwater Navigation](https://mlsanity.com/paper/2502.16510/) (2025)
- [AUV Acceleration Prediction Using DVL and Deep Learning](https://mlsanity.com/paper/2503.16573/) (2025)
- [Canine Clinical Gait Analysis for Orthopedic and Neurological Disorders: An Inertial Deep-Learning Approach](https://mlsanity.com/paper/2507.05671/) (2025)
- [Diffusion Denoiser-Aided Gyrocompassing](https://mlsanity.com/paper/2507.21245/) (2025)
- [Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometers](https://mlsanity.com/paper/2511.13071/) (2025)
- [ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM](https://mlsanity.com/paper/2512.00037/) (2025)
- [Airborne Magnetic Anomaly Navigation with Neural-Network-Augmented Online Calibration](https://mlsanity.com/paper/2603.08265/) (2026)
- [Neural-Assisted in-Motion Self-Heading Alignment](https://mlsanity.com/paper/2604.00168/) (2026)
- [Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation](https://mlsanity.com/paper/2605.18704/) (2026)
- [Manifold Navigation Learning Using Recurrent Networks: Efficient Training Techniques](https://mlsanity.com/paper/2610.14325/) (2026)

## Abstract (arXiv)

Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing and sensor fusion. This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. These data-driven approaches mainly aim to empower model-based inertial sensing algorithms. To encourage further research in integrating deep learning with inertial navigation and fusion and to leverage their capabilities, this paper provides an in-depth review of deep learning methods for inertial sensing and sensor fusion. We discuss learning methods for calibration and denoising as well as approaches for improving pure inertial navigation and sensor fusion. The latter is done by learning some of the fusion filter parameters. The reviewed approaches are classified by the environment in which the vehicles operate: land, air, and sea. In addition, we analyze trends and future directions in deep learning-based navigation and provide statistical data on commonly used approaches.